Inverse Kinematic Solutions Using Artificial Neural Networks

2014 ◽  
Vol 534 ◽  
pp. 137-143 ◽  
Author(s):  
Iskandar Petra ◽  
Liyanage C. de Silva
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Inverse Kinematics ◽  
Joint Angle ◽  
Inverse Kinematic ◽  
Task Execution ◽  
Advantages And Disadvantages ◽  
Complex Process ◽  
Inverse Kinematics Problem ◽  
Artificial Neural

Inverse Kinematics solutions are needed for control of robotic manipulators for successful task execution. It is the process of obtaining the required manipulator joint angle values for a given desired end point position and orientation. In general the process of obtaining these joint angle values is a complex process that may require some higher computational power in the hardware. Mainly there are three traditional methods used to solve inverse kinematics problem, namely; geometric methods, algebraic methods and iterative methods. Apart from these traditional techniques researchers have looked into the use of Artificial Neural Networks (ANNs). In this paper we re-visit these non-traditional techniques and compare the advantages and disadvantages of each method.

A simulation-based method using artificial neural networks for solving the inverse kinematic problem of articulated robots

2015 ◽  
Vol 231 (3) ◽  
pp. 470-479 ◽  
Author(s):  
Mustafa Soylak ◽  
Tuğrul Oktay ◽  
İlke Turkmen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Inverse Kinematic ◽  
Plasma Cutting ◽  
Three Dimensional Model ◽  
Inverse Kinematic Problem ◽  
Data Set ◽  
Artificial Neural

In our article, inverse kinematic problem of a plasma cutting robot with three degree of freedom is solved using artificial neural networks. Artificial neural network was trained using joint angle values according to cartesian coordinates ( x, y, z) of end point of a robotic arm. The Levenberg–Marquardt training algorithm was applied to educate artificial neural network. To validate the designed neural network, it was tested using a new test data set which is not applied in training. A simulation was performed on a three-dimensional model of MSC.ADAMS software using angle values obtained from artificial neural network test. It was revealed from this simulation that trajectory of plasma cutting torch obtained using artificial neural network agreed well with desired trajectory.

Learning in Artificial Neural Networks: A Statistical Perspective

1989 ◽  
Vol 1 (4) ◽  
pp. 425-464 ◽  
Author(s):  
Halbert White
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Optimization Theory ◽  
Comprehensive Treatment ◽  
Training Methods ◽  
Network Learning ◽  
Advantages And Disadvantages ◽  
Systems Identification ◽  
Artificial Neural ◽  
Insight Into

The premise of this article is that learning procedures used to train artificial neural networks are inherently statistical techniques. It follows that statistical theory can provide considerable insight into the properties, advantages, and disadvantages of different network learning methods. We review concepts and analytical results from the literatures of mathematical statistics, econometrics, systems identification, and optimization theory relevant to the analysis of learning in artificial neural networks. Because of the considerable variety of available learning procedures and necessary limitations of space, we cannot provide a comprehensive treatment. Our focus is primarily on learning procedures for feedforward networks. However, many of the concepts and issues arising in this framework are also quite broadly relevant to other network learning paradigms. In addition to providing useful insights, the material reviewed here suggests some potentially useful new training methods for artificial neural networks.

MODULAR NEURAL NETWORKS: A SURVEY

1999 ◽  
Vol 09 (02) ◽  
pp. 129-151 ◽  
Author(s):  
GASSER AUDA ◽  
MOHAMED KAMEL
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Artificial Neural Networks ◽  
Decomposition Techniques ◽  
Task Decomposition ◽  
Modular Neural Networks ◽  
Advantages And Disadvantages ◽  
Artificial Neural ◽  
Learning Schemes

Modular Neural Networks (MNNs) is a rapidly growing field in artificial Neural Networks (NNs) research. This paper surveys the different motivations for creating MNNs: biological, psychological, hardware, and computational. Then, the general stages of MNN design are outlined and surveyed as well, viz., task decomposition techniques, learning schemes and multi-module decision-making strategies. Advantages and disadvantages of the surveyed methods are pointed out, and an assessment with respect to practical potential is provided. Finally, some general recommendations for future designs are presented.

Back Propagation Method of Artificial Neural Networks for Finding the Position Control of Stanford Manipulator and Direct Kinematic Analysis of Elbow Manipulator

2018 ◽  
Vol 7 (1) ◽  
pp. 46 ◽  
Author(s):  
M. Sailaja ◽  
R. D. V. Prasad
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Inverse Kinematics ◽  
Back Propagation ◽  
Network Approach ◽  
Neural Network Approach ◽  
Artificial Neural ◽  
Direct Kinematics

Nowadays the robot technology is advancing rapidly and the use of robots in industries has been increasing. In designing a robot manipulator, kinematicsplays a vital role. The kinematic problem of manipulator control is divided into two types, direct kinematics and inverse kinematics. Robot inverse kinematics, which is important in robot path planning, is a fundamental problem in robotic control. Past solutions for this problem have been through the use of various algebraic or algorithmic procedures, which may be less accurate and time consuming. Artificial neural networks have the ability to approximate highly non-linear functions applied in robot control. The neural network approach deserves examination because of the fundamental properties of computation speed, and they can generalize untrained solutions. In the present work an attempt has been made to evaluate the problemof robot inverse kinematics of Stanford manipulator using artificial neural network approach. Finally two programs are written using C language to solve inverse kinematic problem of Stanford manipulator using Back propagation method of artificial neural network. In this network, the input layer has six nodes, the hidden layer has three nodes, and the output layer has two nodes. And also Elbow manipulator was modelled and its direct kinematics was analysed.

THE SYSTEM OF AUTOMATED DEVELOPMENT, LEARNING AND EXECUTION OF ARTIFICIAL NEURAL NETWORKS

2019 ◽  
pp. 100-107
Author(s):  
V.A. Sobolevsky
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Automated Generation ◽  
Advantages And Disadvantages ◽  
Automated Learning ◽  
Ice Drift ◽  
Artificial Neural ◽  
The Creation

Goal: the need for systems of automated generation of models of complexly formalized objects is considered. The approach to the creation of such a system based on deep learning is described. Materials and methods: the article describes the architecture of the application of automated learning, based on deep learning, in particular on the basis of the genetic algorithm. Results: the testing of the presented system was carried out on the example of solving the problem of predicting the parameters of ice drift on the Northern Dvina River. Conclusion: the advantages and disadvantages, features of implementation, the scope of the presented system are shown.

A novel optimization robust design of artificial neural networks to solve the inverse kinematics of a manipulator of 6 DOF

2021 ◽  
Author(s):  
Teodoro Ibarra-Perez ◽  
Ma. Del Rosario Martinez-Blanco ◽  
Fernando Olivera-Domingo ◽  
Jose Manuel Ortiz-Rodriguez ◽  
Javier Gomez-Escribano
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Robust Design ◽  
Inverse Kinematics ◽  
Artificial Neural

Heliport Detection Using Artificial Neural Networks

2020 ◽  
Vol 86 (9) ◽  
pp. 541-546
Author(s):  
Emre Başeski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Learning Approaches ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Learning Techniques ◽  
Critical Technology ◽  
Artificial Neural ◽  
Military Facilities

Automatic image exploitation is a critical technology for quick content analysis of high-resolution remote sensing images. The presence of a heliport on an image usually implies an important facility, such as military facilities. Therefore, detection of heliports can reveal critical information about the content of an image. In this article, two learning-based algorithms are presented that make use of artificial neural networks to detect H-shaped, light-colored heliports. The first algorithm is based on shape analysis of the heliport candidate segments using classical artificial neural networks. The second algorithm uses deep-learning techniques. While deep learning can solve difficult problems successfully, classical-learning approaches can be tuned easily to obtain fast and reasonable results. Therefore, although the main objective of this article is heliport detection, it also compares a deep-learning based approach with a classical learning-based approach and discusses advantages and disadvantages of both techniques.

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